360dissertations understands how tough Masters level projects can be. The standard of a post graduate course is higher than a bachelor’s degree.

A hypotheses is an assumption about a population parameter which necessarily may not be true. The starting of a dissertation research is hunches, guesses and questions, which can be either true or untrue. In fact that is the purpose of a research. The research hypotheses states ones expectations in a positive manner. The key steps for hypotheses testing are:

  1. In the first step we state the null and alternate hypotheses clearly. The difference between the two is that, the null hypotheses signifies that the results of the observation of sample are pure by chance whereas the alternate hypotheses says that the results are obtained by non-random causes and not based on chance. The null and alternate hypotheses are labelled as H0 and H1 respectively.
  2. In the second step we determine the test size, the researcher has to know whether the test is single tailed or two tailed. This is done so that the critical value and rejection region for accepting or rejecting the hypotheses are known to the researcher. A single tailed test is the one in which the region of rejection is only on a single side of the sampling distribution and a two tailed test is the one region for rejection can lie on both sides of the sampling distribution.
  3. After having done that, in the third step,the researcher has to find the statistical value. It could be the mean score, T score, Z score or whatever is required to be identified. In addition the probability value has to be also determined. The researcher has to finalise the significance value which usually is among 0.01, 0.05 or 0.10. What method of test will be deployed has to be selected carefully, based on the sampling distribution. Z test, T test, Chi Square, Regression, ANOVA, Means are some of the commonly used tests at this stage.
  4. Once the tests are conducted, the interpretation of the score would determine whether the researcher would accept or reject the null hypotheses. The thumb rule is that if the P value is less than the chosen significance value then we reject the null hypotheses. Often errors are committed at this stage and the most commonly found errors are type I and type II errors.
  5. In the next step, you need draw conclusion from the chosen data by applying the decision rule that was prescribed in the analysis plan in the formative stages.

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